CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

TL;DR

This paper introduces a novel medical image registration framework that integrates equivariant contrastive learning to learn deformation-invariant features, significantly improving registration accuracy across various scenarios.

Contribution

It proposes a joint optimization approach combining contrastive learning with registration, enhancing robustness and accuracy in medical image alignment.

Findings

Outperforms baseline registration methods on abdominal and thoracic datasets.

Learned features are invariant to tissue deformations, improving robustness.

Joint training enhances registration performance in intra- and inter-patient scenarios.

Abstract

Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsistencies and nonlinear tissue deformations pose significant challenges to the robustness of registration methods. Recent approaches leveraging self-supervised representation learning show promise by pre-training feature extractors to generate robust anatomical embeddings, that farther used for the registration. In this work, we propose a novel framework that integrates equivariant contrastive learning directly into the registration model. Our approach leverages the power of contrastive learning to learn robust feature representations that are invariant to tissue deformations. By jointly optimizing the contrastive and registration objectives, we ensure that the learned representations are not only informative but also suitable for the registration task. We evaluate our method on abdominal and thoracic image registration tasks, including both intra-patient and inter-patient scenarios. Experimental results demonstrate that the integration of contrastive learning directly into the registration framework significantly improves performance, surpassing strong baseline methods.